• 11/05/2003
    3:00 PM
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Storage Pipeline: Review: Out of the Abyss

Managing massive amounts of data on mix-and-match systems is an overwhelming task.

Enter storage-resource management, an approach Legato (now a division of EMC), Tek-Tools, Veritas and several other vendors tout with their various suites and frameworks. SRM comprises a collection of software tools for discovering where storage is located and how its capacity is being used. The more sophisticated products monitor storage hardware capacity utilization and other storage-related software processes, such as data replication, volume management and backup, to provide alerts about potential problems in near real time.

SRM Timeline

At the turn of the century, SRM was touted as a silver bullet. The software was to enable fewer managers to oversee more storage and to provide ample warning so that steps could be taken to solve problems before they created downtime. By 2001, however, SRM came under attack for being too platform-focused to address the real goals of storage management. Critics said you couldn't manage storage by taking the temperature of various infrastructure components; you needed to look at what effect storage was having on application performance. BMC Software spearheaded efforts to deliver an application-centric, policy-based approach to storage management that could drive labor costs out of storage investments.

Many SRM vendors seized upon this idea to create complex products typified by an automated, application-aware storage-management software stack. These stacks comprised a policy engine riding atop a storage-virtualization layer (virtual volume managers for block storage and global namespace products for file systems) that automated certain tasks, such as growing virtual volumes to meet burgeoning application data storage requirements, and improved single manager productivity. Work continues in this area as many brand-name management-software vendors begin porting their technology into storage-switching platforms or multifunction storage-management appliances.

But the story doesn't end there. Over the past year, vendors have started chanting yet another mantra: data life-cycle management. In this approach, information about applications and storage platform capabilities and costs is used to create a knowledge base that lets policy engines create rules for automating the smooth migration of data across different storage devices. Instead of focusing on the application or storage infrastructure solely, this model makes data its centerpiece.

According to EMC's Legato and others embracing this vision, the data life-cycle management revolution will be more effective at automating management and reducing costs than any hardware or software innovation to date. It will create the optimal storage-utility infrastructure, in which capacity is allocated dynamically and automatically to applications that need it, and data will move from platform to platform automatically, based on its usage characteristics, retention requirements, platform costs and other factors.

But there are limitations in the life-cycle management products now emerging. One of the most nagging is the absence of an open standard for equipping data with a self-descriptive header that would identify its requirements or originating application. This header would help automated management tools move the data around. Currently, all the approaches for data self-description are proprietary and limited. However, work is under way at both NASA's Jet Propulsion Laboratory and the International Standards Organization to create a standardized naming convention (see

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